NLP & RAG System Development
Production NLP pipelines and RAG systems that ground LLM outputs in enterprise knowledge. From named entity recognition to hybrid vector search and document intelligence at scale. Projects start at $5,000.
- 10+ AI PRODUCTS SHIPPED TO PRODUCTION
- 100K+ WORLDWIDE USERS SERVED
- 4.8★ AVERAGE CLIENT RATING
- VOCALO.AI + QUIZMAKERAI: LIVE RAG PIPELINES IN PRODUCTION
- 5+ YEARS ACTIVE
- 15 AI ENGINEERS
- GLOBAL DELIVERY FROM BANGLADESH
Natural language processing (NLP) enables computers to analyse and generate human language through tokenisation, entity recognition, text classification, and semantic parsing. Retrieval-Augmented Generation (RAG) augments LLM generation by retrieving relevant context from a vector knowledge base, grounding outputs in enterprise specific data. For your team, that replaces LLM hallucination with grounded responses traced to specific documents. Riverborn builds production-grade NLP pipelines and RAG systems, validated across 100K+ users via Vocalo.ai and QuizMakerAI.
What Your Team Gets
across all eight stages: ingestion, chunking, embedding, vector indexing (Pinecone, Weaviate, pgvector), hybrid retrieval, reranking, and generation. So your LLM answers from your enterprise knowledge, not training data.
combining BM25 keyword and semantic vector search via Reciprocal Rank Fusion (RRF). Outperforms semantic-only retrieval by 15 to 25% on recall@10 (BEIR benchmark).
for NER, text classification, sentiment analysis, intent detection, and keyword extraction using spaCy, BERT, and RoBERTa.
PDFs, contracts, and manuals processed via GPT-4o vision and Azure Document Intelligence. Extracted data feeds into the vector knowledge base for RAG queries.
fragmented data (SharePoint, Confluence, Notion, databases, email archives) unified into a single agent-accessible vector knowledge base via MCP.
(faithfulness, answer relevance, context precision) on scheduled production test suites. Projects start at $5,000.
What We Build: NLP and RAG System Types
Riverborn delivers five categories of RAG development services and NLP development services. Each system type specifies the capability, the mechanism, and the enterprise application.
NLP and RAG system development is a category within applied AI engineering focused on language understanding and knowledge retrieval. NLP types include NER, text classification, sentiment analysis, intent detection, and semantic search. RAG types include document RAG, conversational RAG, multi hop RAG, and agentic RAG.
RAG Knowledge Systems
Full eight-stage RAG pipeline (detailed in the RAG Pipeline Architecture table below). Primary applications are LLM chatbots, AI agents, and document Q&A systems grounded in proprietary enterprise knowledge.
Semantic Search Systems
Semantic search replaces traditional keyword search using vector embeddings and cosine similarity retrieval. Enterprise applications include product search, HR policy retrieval, legal precedent search, and technical documentation lookup.
Document Intelligence Pipelines
Processes unstructured documents via GPT-4o vision (PDFs, scanned contracts) and Azure Document Intelligence (forms, tables). Extracted data feeds into the vector knowledge base. Use cases: contract analysis, invoice processing, regulatory classification.
NLP Text Processing Pipelines
spaCy and Hugging Face Transformers pipelines for NER, classification, sentiment, intent detection, and keyword extraction. BERT and RoBERTa at production scale. Applications: CRM enrichment, support ticket routing, compliance monitoring, and market intelligence.
Enterprise Knowledge Fabric Construction
Maps enterprise knowledge sources (SharePoint, Notion, Confluence, databases, email archives), designs chunking and embedding per source, and implements multi-tenant vector namespaces. Connects to AI agents via MCP.
Our Technical Approach: RAG Pipeline Architecture
Gartner’s 2025 report projects 80% of enterprise LLM deployments will use RAG by 2026. Riverborn implements all eight stages with RAGAS evaluation as standard.
| Stage | Name | Description and Technology |
|---|---|---|
| 1 | Ingestion | Connectors pull documents from SharePoint, Confluence, S3, databases, and email. Supported formats: PDF, DOCX, XLSX, HTML, Markdown, plain text. Incremental ingestion via change detection. |
| 2 | Chunking | Fixed size chunks (512 tokens, 50-token overlap), semantic chunking (sentence-boundary-aware), or hierarchical chunking (parent-child for context preservation). Strategy selected by document type and query pattern. |
| 3 | Embedding | Text embedding-3-large (OpenAI, 3072-dim, highest accuracy), BGE-M3 (open source, multilingual, 1024-dim), or Cohere Embed v3 (768-dim, compression optimised). Batch processing for cost efficiency. |
| 4 | Vector Indexing | Pinecone (managed, serverless, sub-10ms p99), Weaviate (self hosted, multi-tenancy, GraphQL), Qdrant (high performance metadata filtering, HNSW index), or pgvector (PostgreSQL-native). |
| 5 | Retrieval | Hybrid retrieval combines BM25 keyword search with semantic cosine similarity via Reciprocal Rank Fusion (RRF), capturing both exact match and conceptual similarity relevance. |
| 6 | Reranking | Top-k chunks (typically k=20) scored by cross-encoder: Cohere Rerank, BGE Reranker v2, or ms marco MiniLM. Precision improves from 60 to 70% to 85 to 92%. |
| 7 | Generation | GPT-4o, Claude Sonnet 4, or Llama 3.1 70B, selected by output quality, compliance constraints, and cost per query budget. Structured context formatting with source attribution. |
| 8 | RAGAS Evaluation | Faithfulness, Answer Relevance, and Context Precision (Shahul Es et al., 2023). Continuous evaluation via scheduled production test suites. |
Ingestion
Connectors pull documents from SharePoint, Confluence, S3, databases, and email. Supported formats: PDF, DOCX, XLSX, HTML, Markdown, plain text. Incremental ingestion via change detection.
Chunking
Fixed size chunks (512 tokens, 50-token overlap), semantic chunking (sentence-boundary-aware), or hierarchical chunking (parent-child for context preservation). Strategy selected by document type and query pattern.
Embedding
Text embedding-3-large (OpenAI, 3072-dim, highest accuracy), BGE-M3 (open source, multilingual, 1024-dim), or Cohere Embed v3 (768-dim, compression optimised). Batch processing for cost efficiency.
Vector Indexing
Pinecone (managed, serverless, sub-10ms p99), Weaviate (self hosted, multi-tenancy, GraphQL), Qdrant (high performance metadata filtering, HNSW index), or pgvector (PostgreSQL-native).
Retrieval
Hybrid retrieval combines BM25 keyword search with semantic cosine similarity via Reciprocal Rank Fusion (RRF), capturing both exact match and conceptual similarity relevance.
Reranking
Top-k chunks (typically k=20) scored by cross-encoder: Cohere Rerank, BGE Reranker v2, or ms marco MiniLM. Precision improves from 60 to 70% to 85 to 92%.
Generation
GPT-4o, Claude Sonnet 4, or Llama 3.1 70B, selected by output quality, compliance constraints, and cost per query budget. Structured context formatting with source attribution.
RAGAS Evaluation
Faithfulness, Answer Relevance, and Context Precision (Shahul Es et al., 2023). Continuous evaluation via scheduled production test suites.
Vector Database Selection Guide
MarketsandMarkets 2024 projects the RAG market at $11.4B by 2030. Vector database selection has the highest operational impact on RAG performance.
| Vector Database | Best For | Key Differentiator | Riverborn Use Case |
|---|---|---|---|
Pinecone | Managed, serverless deployments requiring sub-10ms p99 latency at scale | Fully managed with automatic scaling and no infrastructure operations | Enterprise RAG systems requiring guaranteed SLA and minimal DevOps |
Weaviate | Self-hosted deployments requiring multi-tenancy and GraphQL API access | Hybrid search built-in (BM25 + semantic), module system for reranking | Enterprise knowledge fabrics requiring data residency control |
Qdrant | High-performance filtering on metadata-rich document collections | HNSW index with payload filtering and sparse vector support | Document intelligence systems with complex metadata filtering |
pgvector | Organisations with existing PostgreSQL infrastructure | Native PostgreSQL extension: same database, same operations, same backups | Startup RAG systems where minimal infrastructure footprint is a priority |
Pinecone
Best For
Managed, serverless deployments requiring sub-10ms p99 latency at scale
Key Differentiator
Fully managed with automatic scaling and no infrastructure operations
Use Case
Enterprise RAG systems requiring guaranteed SLA and minimal DevOps
Weaviate
Best For
Self-hosted deployments requiring multi-tenancy and GraphQL API access
Key Differentiator
Hybrid search built-in (BM25 + semantic), module system for reranking
Use Case
Enterprise knowledge fabrics requiring data residency control
Qdrant
Best For
High-performance filtering on metadata-rich document collections
Key Differentiator
HNSW index with payload filtering and sparse vector support
Use Case
Document intelligence systems with complex metadata filtering
pgvector
Best For
Organisations with existing PostgreSQL infrastructure
Key Differentiator
Native PostgreSQL extension: same database, same operations, same backups
Use Case
Startup RAG systems where minimal infrastructure footprint is a priority
RAG vs Fine-Tuning: Selection Framework
Gartner’s 2025 report shows 67% hallucination reduction with RAG versus retrieval-free deployments. Riverborn recommends RAG for most enterprise use cases.
| Dimension | RAG | Fine-Tuning |
|---|---|---|
Knowledge type | Dynamic, frequently updated knowledge (catalogues, policies, news) | Stable, domain-specific task knowledge (legal clause extraction, medical code classification) |
Data volume | Works with as few as 100 documents | Requires 500 to 50,000+ labelled examples |
Update frequency | Real time via ingestion pipeline without retraining | Model retraining required on every significant knowledge update |
Compliance context | Retrieval sources are auditable: every answer traces to specific documents | Knowledge baked into model weights: individual examples cannot be easily audited |
Latency | Adds retrieval latency (50 to 200ms for Pinecone, 100 to 300ms for Weaviate) | Inference latency unchanged, better for sub-100ms p99 requirements |
Recommendation | Start with RAG for most enterprise use cases | Fine-tune only when stable specialisation, sufficient data, and hard latency constraints all apply |
Knowledge type
RAG
Dynamic, frequently updated knowledge (catalogues, policies, news)
Fine-Tuning
Stable, domain-specific task knowledge (legal clause extraction, medical code classification)
Data volume
RAG
Works with as few as 100 documents
Fine-Tuning
Requires 500 to 50,000+ labelled examples
Update frequency
RAG
Real time via ingestion pipeline without retraining
Fine-Tuning
Model retraining required on every significant knowledge update
Compliance context
RAG
Retrieval sources are auditable: every answer traces to specific documents
Fine-Tuning
Knowledge baked into model weights: individual examples cannot be easily audited
Latency
RAG
Adds retrieval latency (50 to 200ms for Pinecone, 100 to 300ms for Weaviate)
Fine-Tuning
Inference latency unchanged, better for sub-100ms p99 requirements
Recommendation
RAG
Start with RAG for most enterprise use cases
Fine-Tuning
Fine-tune only when stable specialisation, sufficient data, and hard latency constraints all apply
Industries and Use Cases
Every engagement specifies knowledge source, chunking strategy, embedding model, and vector database before build begins.
Legal Services: Contract Intelligence RAG
Q&A across 50,000+ contracts. Hierarchical RAG with GPT-4o vision ingestion, text-embedding-3-large, Weaviate metadata filters. Hybrid retrieval with Cohere Rerank. Claude Sonnet 4 generates cited responses. RAGAS faithfulness: 0.91. Query time: 3.2s p95.
Healthcare: Clinical Knowledge Fabric
Clinical staff access to 200+ PDF policy and formulary documents. Semantic search using Azure Document Intelligence, BGE-M3, and pgvector. GPT-4o generates cited responses. Vocalo.ai RAG architecture served as production proof.
E-Commerce: Product Intelligence NLP
Automated catalogue enrichment across 10,000+ SKUs. spaCy + RoBERTa pipeline for NER, zero-shot classification, and aspect-based sentiment analysis. Throughput: 10,000 products per hour. Classification accuracy: 93.4%.
Enterprise SaaS: Internal Knowledge Search
Search across 5,000+ Confluence pages, 200+ Notion docs, and 3 years of Slack archives. Enterprise knowledge fabric with multi-source connectors, semantic chunking, Pinecone multi-namespace index. Hybrid RRF retrieval feeds GPT-4o. Employee satisfaction: 89%, up from 34%.
Technology Stack
Every NLP and RAG engagement ships on the production validated stack below.
| Stack Layer | Technologies & Frameworks |
|---|---|
NLP Models & Frameworks | spaCy (NER, dependency parsing)Hugging Face Transformers (BERT, RoBERTa, DeBERTa)NLTK (text preprocessing)Presidio (PII detection) |
Embedding Models | Text embedding-3-large (OpenAI, 3072-dim)BGE-M3 (open source, multilingual)Cohere Embed v3 (768-dim)sentence-transformers |
Vector Databases | Pinecone (managed)Weaviate (self hosted)Qdrant (metadata filtering)pgvector (PostgreSQL-native) |
Retrieval & Reranking | BM25 (Elasticsearch, Qdrant sparse vectors)Semantic cosine similarityRRFCohere RerankBGE Reranker v2ms marco MiniLM |
Document Processing | GPT-4o vision (PDFs, images)Azure Document Intelligence (forms, tables)Tesseract OCRLlamaParse (complex PDF layouts) |
Generation Models | GPT-4oClaude Sonnet 4Llama 3.1 70B (selected by quality, compliance, cost) |
Orchestration | LangChain (RAG pipeline)LlamaIndex (knowledge base construction)LangGraph (agentic RAG) |
Evaluation | RAGAS framework (faithfulness, relevance, context precision)Custom eval suitesLangSmith (tracing and debugging) |
NLP Models & Frameworks
Embedding Models
Vector Databases
Retrieval & Reranking
Document Processing
Generation Models
Orchestration
Evaluation
How It Works: Our Delivery Process
Five-step delivery process. Single knowledge base RAG completes in 4 to 6 weeks. Enterprise knowledge fabric completes in 8 to 12 weeks.
Knowledge Architecture Design
Audit of knowledge sources (document types, volumes, update frequency). Chunking strategy, embedding model, vector database, and retrieval architecture selected and documented with rationale before engineering begins.
Deliverable
Ingestion Pipeline Build
Connectors built per knowledge source. Chunking applied per document type. Embeddings generated, vector index populated. RAGAS baseline scores established.
Deliverable
Retrieval and Reranking Implementation
Hybrid search (BM25 + semantic + RRF) implemented. Reranking model integrated and tuned. Retrieval parameters calibrated against BEIR-style benchmarks.
Deliverable
Generation and Prompt Engineering
Context assembly and prompt engineering optimised for grounded generation with source attribution. RAG system integrates with the target application (chatbot, agent, or search).
Deliverable
Production Deployment and Monitoring
Production deployment with LangSmith tracing and quality dashboards. Incremental ingestion on defined schedule. RAGAS evaluation suites run continuously.
Deliverable
Why Riverborn
Gartner’s 2025 report shows 67% hallucination reduction with RAG versus retrieval-free deployments. The gap between demo RAG and production RAG is retrieval optimisation, reranking, and continuous RAGAS evaluation.
PRODUCTION RAG FROM A TEAM THAT SHIPS
Production RAG from a team that ships.
Vocalo.ai and QuizMakerAI use Riverborn-built RAG pipelines serving 100K+ users. The same hybrid search and RAGAS architecture deployed in client systems runs in Riverborn's own products.
VENDOR-NEUTRAL STACK SELECTION
Vendor-neutral stack selection.
Pinecone for managed scale, Weaviate for data residency, Qdrant for metadata filtering, pgvector for PostgreSQL-native. Selected against your query volume, infrastructure, and latency requirements.
HYBRID SEARCH AND RAGAS AS STANDARD
Hybrid search and RAGAS evaluation as standard deliverables.
Most RAG implementations use semantic-only retrieval and measure quality anecdotally. Riverborn implements hybrid search (BM25 + semantic + RRF) and RAGAS evaluation on every engagement.
COST STRUCTURE
A cost structure that makes production RAG accessible.
Riverborn's Bangladesh delivery model provides production-grade RAG development company capability at 40 to 60% of the cost of comparable US and EU NLP/RAG implementation firms. Projects start at $5,000.
4.8+ avg. client rating
Hybrid search (BM25 + semantic + RRF) on every RAG engagement
100K+ global users on Riverborn-built RAG pipelines
RAGAS evaluation as standard production deliverable
Pinecone, Weaviate, Qdrant, pgvector: all four vector databases in production
40 to 60% cost advantage vs. US/EU NLP/RAG vendors
Related Services
AI Chatbot Development
RAG pipelines power production AI chatbots. Riverborn's AI chatbot development service builds the conversational interface on this RAG infrastructure.
Learn more →Generative AI Development
Generative AI applications use RAG as their retrieval layer. Riverborn's generative AI development service builds both the application and the RAG infrastructure.
Learn more →AI Agent Development
AI agents use RAG as long-term memory. Riverborn's AI agent development service integrates with RAG systems via LangChain retriever and MCP tool interface.
Learn more →Industries We Serve
Riverborn’s NLP and RAG deployments concentrate in healthcare, financial services, and SaaS and technology. Each vertical is covered with architecture-specific use case patterns in the Industries and Use Cases section above.
Frequently Asked Questions
Retrieval-Augmented Generation (RAG) augments LLM generation by retrieving relevant context from a vector-indexed knowledge base. Outputs are grounded in enterprise-specific data rather than training knowledge alone. RAG addresses LLM hallucination by providing the generation model with retrieved, auditable document chunks as context. Riverborn's RAG pipelines power Vocalo.ai and QuizMakerAI across 100K+ production users.
RAG retrieves dynamic knowledge at query time and grounds LLM outputs in auditable document sources. Fine-tuning bakes static task knowledge into model weights. RAG suits dynamic, frequently updated knowledge. Fine-tuning suits stable domain specialisation with hard latency constraints. Most production systems start with RAG.
A vector database stores high-dimensional embedding vectors and retrieves semantically similar vectors using approximate nearest neighbour (ANN) algorithms. RAG requires a vector database to store document chunk embeddings and retrieve the most relevant chunks for any user query. Riverborn implements four vector databases: Pinecone (managed), Weaviate (self-hosted), Qdrant (metadata filtering), and pgvector (PostgreSQL-native).
Hybrid search combines BM25 keyword search with semantic vector search via Reciprocal Rank Fusion (RRF). BM25 captures exact-match relevance that pure semantic search misses. BEIR benchmark research shows hybrid search outperforms semantic-only retrieval by 15 to 25% on recall@10. Riverborn implements hybrid search on every RAG engagement as standard.
RAGAS (Shahul Es et al., 2023) is an automated evaluation framework measuring three RAG quality dimensions: Faithfulness (answer grounded in context), Answer Relevance (answer addresses query), and Context Precision (retrieved chunks are relevant). Riverborn deploys RAGAS on scheduled production test suites as a standard deliverable.
Riverborn builds NLP pipelines for named entity recognition (NER), text classification, sentiment analysis, intent detection, keyword extraction, dependency parsing, and text summarisation. Frameworks used: spaCy (NER, dependency parsing), Hugging Face Transformers (BERT, RoBERTa, DeBERTa), and NLTK (text preprocessing). Applications: CRM enrichment, support ticket routing, compliance monitoring, and market intelligence.
A focused single knowledge base RAG system completes in 4 to 6 weeks across five phases: Knowledge Architecture Design, Ingestion Pipeline Build, Retrieval and Reranking Implementation, Generation and Prompt Engineering, and Production Deployment and Monitoring. An enterprise knowledge fabric covering multiple sources and tenants completes in 8 to 12 weeks. Projects start at $5,000.
Yes. Riverborn's RAG pipelines integrate with AI agent architectures via the LangChain retriever interface and MCP tool interface. Agents gain real-time access to the enterprise knowledge fabric through either integration path. The same integration connects RAG systems to LLM chatbot applications. Riverborn's AI agent development service and AI chatbot development service consume RAG infrastructure directly.